1 👥 Cohort Description
Cohort Overview
| Characteristic | N = 1471 |
|---|---|
| Age | – |
| Median (Q1, Q3) | 37 (34, 44) |
| Unknown | 13 |
| Age group | – |
| age ≥15-<25 | 1/134 (0.7%) |
| age ≥25-<35 | 45/134 (34%) |
| age ≥35-<45 | 62/134 (46%) |
| age ≥45-<55 | 19/134 (14%) |
| age ≥55 | 7/134 (5.2%) |
| Unknown | 13 |
| Sex | – |
| Female | 38/134 (28%) |
| Male | 96/134 (72%) |
| Unknown | 13 |
| Period | – |
| First Three Days | 74/147 (50%) |
| Last Two Days | 73/147 (50%) |
| Years of Professional Experience | – |
| Median (Q1, Q3) | 11 (8, 17) |
| Unknown | 13 |
| Experience in VHF Response | 101/134 (75%) |
| Unknown | 13 |
| The IDTM is an Advantage to the Systems Used in the Past | 117/121 (97%) |
| Unknown | 26 |
| Envisioning Usage of IDMT in the Future | – |
| Yes | 121/121 (100%) |
| Unknown | 26 |
| 1 n/N (%) | |
Age and Sex Distribution
Cohort by Period
| Characteristic | Overall N = 1471 |
First Three Days N = 741 |
Last Two Days N = 731 |
p-value |
|---|---|---|---|---|
| Age | – | – | – | – |
| Median (Q1, Q3) | 37 (34, 44) | 37 (34, 43) | 37 (34, 44) | – |
| Unknown | 13 | 8 | 5 | – |
| Age group | – | – | – | – |
| age ≥15-<25 | 1/134 (0.7%) | 1/66 (1.5%) | 0/68 (0%) | – |
| age ≥25-<35 | 45/134 (34%) | 22/66 (33%) | 23/68 (34%) | – |
| age ≥35-<45 | 62/134 (46%) | 32/66 (48%) | 30/68 (44%) | – |
| age ≥45-<55 | 19/134 (14%) | 8/66 (12%) | 11/68 (16%) | – |
| age ≥55 | 7/134 (5.2%) | 3/66 (4.5%) | 4/68 (5.9%) | – |
| Unknown | 13 | 8 | 5 | – |
| Sex | – | – | – | – |
| Female | 38/134 (28%) | 18/66 (27%) | 20/68 (29%) | – |
| Male | 96/134 (72%) | 48/66 (73%) | 48/68 (71%) | – |
| Unknown | 13 | 8 | 5 | – |
| Years of Professional Experience | – | – | – | – |
| Median (Q1, Q3) | 11 (8, 17) | 11 (8, 17) | 12 (8, 18) | – |
| Unknown | 13 | 8 | 5 | – |
| Experience in VHF Response | 101/134 (75%) | 50/66 (76%) | 51/68 (75%) | – |
| Unknown | 13 | 8 | 5 | – |
| The IDTM is an Advantage to the Systems Used in the Past | 117/121 (97%) | 58/61 (95%) | 59/60 (98%) | – |
| Unknown | 26 | 13 | 13 | – |
| Envisioning Usage of IDMT in the Future | – | – | – | – |
| Yes | 121/121 (100%) | 61/61 (100%) | 60/60 (100%) | – |
| Unknown | 26 | 13 | 13 | – |
| 1 n/N (%) | ||||
2 📊 Survey Overview
Overall, 96.7% of participants considered IDTM an advantage to the systems used in the past and 100% are envisioning the usage of IDMT in the future (see Cohort Description above).
2.1 UEQ Scales Summary
| Category | Average1 | Std Dev. | Std Err. | 95% CI | 2 |
|---|---|---|---|---|---|
| Attractiveness | 2.337 | 0.867 | 0.079 | 2.18-2.49 | ▲ |
| Perspicuity | 1.853 | 1.078 | 0.098 | 1.66-2.05 | ▲ |
| Novelty | 1.463 | 1.106 | 0.101 | 1.26-1.66 | ▲ |
| Stimulation | 2.324 | 0.916 | 0.083 | 2.16-2.49 | ▲ |
| Dependability | 1.897 | 0.952 | 0.087 | 1.73-2.07 | ▲ |
| Efficiency | 2.132 | 0.942 | 0.086 | 1.96-2.3 | ▲ |
| 1 Values between -0.8and 0.8 represent a more or less neutral evaluation of the corresponding scale, values > 0,8 represent a positive evaluation and values < -0,8 represent a negative evaluation. | |||||
| 2 ▲ Positive Evaluation | ■︎ Neutral | ▼ Negative Evaluation) | |||||
2.2 Scales Distribution
Overall
By period
2.3 UEQ Mean values per items
2.4 Benchmark
The benchmark classifies a product into 5 categories (per scale):
Excellent: In the range of the 10% best results.
Good: 10% of the results in the benchmark data set are better and 75% of the results are worse.
Above average: 25% of the results in the benchmark are better than the result for the evaluated product, 50% of the results are worse.
Below average: 50% of the results in the benchmark are better than the result for the evaluated product, 25% of the results are worse.
Bad: In the range of the 25% worst results.
3 📊 Distribution of Answers per Item
Each item was rated on a 7-point scale from the left concept to the right. Lower values indicate stronger agreement with the first term (e.g., ‘Annoying’), while higher values reflect stronger agreement with the second (e.g., ‘Enjoyable’).
Attractiveness
Perspicuity
Novelty
Stimulation
Dependability
Efficiency
4 💹 Scale structure of the UEQ
5 ⏰ By Period
We used an ordinal logistic regression model to account for the ordered nature of the response scale. This allowed us to estimate the likelihood of each response level across time periods or experience in VHF response and visualize these shifts using the predicted probability plot.
5.1 📈 Attractiveness
Significance of Period Effect
Observed Response
Annoying – Enjoyable
Good – Bad
Unlikable – Pleasing
Unpleasant – Pleasant
Attractive – Unattractive
Friendly – Unfriendly
Predited Probabilities
The predicted probability plot shows the estimated likelihood of each response level across time periods, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences shifted between periods, accounting for the ordered nature of the response scale.
Annoying – Enjoyable
Good – Bad
Unlikable – Pleasing
Unpleasant – Pleasant
Attractive – Unattractive
Friendly – Unfriendly
5.2 📈 Novelty
Significance of Period Effect
Observed Response
Creative – Dull
Inventive – Conventional
Usual – Leading edge
Conservative – Innovative
Predited Probabilities
The predicted probability plot shows the estimated likelihood of each response level across time periods, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences shifted between periods, accounting for the ordered nature of the response scale.
Creative – Dull
Inventive – Conventional
Usual – Leading edge
Conservative – Innovative
5.3 📈 Perspicuity
Significance of Period Effect
Observed Response
Not understandable – Understandable
Easy to learn – Difficult to learn
Complicated – Easy
Clear – Confusing
Predited Probabilities
The predicted probability plot shows the estimated likelihood of each response level across time periods, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences shifted between periods, accounting for the ordered nature of the response scale.
Not understandable – Understandable
Easy to learn – Difficult to learn
Complicated – Easy
Clear – Confusing
5.4 📈 Stimulation
Significance of Period Effect
Observed Response
Valuable – Inferior
Boring – Exciting
Not interesting – Interesting
Motivating – Demotivating
Predited Probabilities
The predicted probability plot shows the estimated likelihood of each response level across time periods, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences shifted between periods, accounting for the ordered nature of the response scale.
Valuable – Inferior
Boring – Exciting
Not interesting – Interesting
Motivating – Demotivating
5.5 📈 Dependability
Significance of Period Effect
Observed Response
Unpredictable – Predictable
Obstructive – Supportive
Secure – Not secure
Meets expectations – Does not meet expectations
Predited Probabilities
The predicted probability plot shows the estimated likelihood of each response level across time periods, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences shifted between periods, accounting for the ordered nature of the response scale.
Unpredictable – Predictable
Obstructive – Supportive
Secure – Not secure
Meets expectations – Does not meet expectations
5.6 📈 Efficiency
Significance of Period Effect
Observed Response
Fast – Slow
Inefficiant – Efficient
Impractical – Practical
Organized – Cluttered
Predited Probabilities
The predicted probability plot shows the estimated likelihood of each response level across time periods, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences shifted between periods, accounting for the ordered nature of the response scale.
Fast – Slow
Inefficiant – Efficient
Impractical – Practical
Organized – Cluttered
6 ⚠️ Inconsistencies
Critical length
Based on the number of identical answers (in the original answer scale in “Data”). If a participant for example crosses for all items the middle category (“4”) this can hardly be accepted as a serious response. Such answers are most likely result from an attempt to quickly finish the survey without much thinking. An analysis of several UEQ data sets suggests a heuristic to remove answers that show for more than 15 items the same response. This heuristic is implemented in the Column “Critical Length”. See the UEQ handbook for details.
Here, we have 4 participants who answered the same answer ≥ 16
Scales with inconsitent answers
To detect such suspicious answers, a simple heuristic is used. All items in a scale should measure a similar UX quality aspect. The idea behind the heuristic is to check how much the best and worst evaluation of an item in a scale differs. If there is a big difference (>3) this is seen as an indicator for a problematic data pattern. Of course such situations can also result from random response errors or a misunderstanding of an item. Thus, it makes no sense to delete a response if this occurs just for a single scale. But if this is true for 2 or 3 scales this is of course a clear hint that the response is somehow suspicious.
Here, we have 22 participants who had large difference between responses (>3) across 2 or 3 scales (highlighted in red below)
7 👨🏽⚕️Experience in VHF Response
7.1 📈 Attractiveness
Significance of the Experience in VHF Response
Observed Response
Annoying – Enjoyable
Good – Bad
Unlikable – Pleasing
Unpleasant – Pleasant
Attractive – Unattractive
Friendly – Unfriendly
Predited Probabilities
The predicted probability plot shows the estimated likelihood of each response level by experience in VHF Response, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences differed between participants with and without experience in VHF response, accounting for the ordered nature of the response scale.
Annoying – Enjoyable
Good – Bad
Unlikable – Pleasing
Unpleasant – Pleasant
Attractive – Unattractive
Friendly – Unfriendly
7.2 📈 Novelty
Significance of the Experience in VHF Response
Observed Response
Creative – Dull
Inventive – Conventional
Usual – Leading edge
Conservative – Innovative
Predited Probabilities
The predicted probability plot shows the estimated likelihood of each response level by experience in VHF Response, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences differed between participants with and without experience in VHF response, accounting for the ordered nature of the response scale.
Creative – Dull
Inventive – Conventional
Usual – Leading edge
Conservative – Innovative
7.3 📈 Perspicuity
Significance of the Experience in VHF Response
Observed Response
Not understandable – Understandable
Easy to learn – Difficult to learn
Complicated – Easy
Clear – Confusing
Predited Probabilities
The predicted probability plot shows the estimated likelihood of each response level by experience in VHF Response, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences differed between participants with and without experience in VHF response, accounting for the ordered nature of the response scale.
Not understandable – Understandable
Easy to learn – Difficult to learn
Complicated – Easy
Clear – Confusing
7.4 📈 Stimulation
Significance of the Experience in VHF Response
Observed Response
Valuable – Inferior
Boring – Exciting
Not interesting – Interesting
Motivating – Demotivating
Predited Probabilities
The predicted probability plot shows the estimated likelihood of each response level by experience in VHF Response, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences differed between participants with and without experience in VHF response, accounting for the ordered nature of the response scale.
Valuable – Inferior
Boring – Exciting
Not interesting – Interesting
Motivating – Demotivating
7.5 📈 Dependability
Significance of the Experience in VHF Response
Observed Response
Unpredictable – Predictable
Obstructive – Supportive
Secure – Not secure
Meets expectations – Does not meet expectations
Predited Probabilities
The predicted probability plot shows the estimated likelihood of each response level by experience in VHF Response, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences differed between participants with and without experience in VHF response, accounting for the ordered nature of the response scale.
Unpredictable – Predictable
Obstructive – Supportive
Secure – Not secure
Meets expectations – Does not meet expectations
7.6 📈 Efficiency
Significance of the Experience in VHF Response
Observed Response
Fast – Slow
Inefficiant – Efficient
Impractical – Practical
Organized – Cluttered
Predited Probabilities
The predicted probability plot shows the estimated likelihood of each response level by experience in VHF Response, as modeled by ordinal logistic regression. It visually illustrates how respondents’ preferences differed between participants with and without experience in VHF response, accounting for the ordered nature of the response scale.
Fast – Slow
Inefficiant – Efficient
Impractical – Practical
Organized – Cluttered
